(c) A fully connected neural network is given below. ● x1 x2 1 Note that: i. ii. iii. w11 w12 w21, w31 w22 w32 al กาอย 1 h12 h21 a2h22 h11 h31 y1 81: y2 The ReLU activation function is defined as follows: h32 For an input x, ReLU(x) = max(0, x) The Softmax function is defined as follows: S Given the inputs, xi, i=1, ..., n; the outputs are as follows: exi = j=1 Calculate the net inputs a1 and a2. Calculate the ReLU outputs. Calculate y1 and y2. Softmax exj Suppose that the weights of the network are w11=-1.5; w12=-1.0, w21-1.2; w22=1.0; w31=-0.6; w32=1.0 h11=-0.5; h12=-1.0; h21=1.0, h22=1.5; h31= 0.4; h32=0.3 For an input of x1= 1.0 and x2=2.0 z1 z2

(c) A fully connected neural network is given below. ● x1 x2 1 Note that: i. ii. iii. w11 w12 w21, w31 w22 w32 al กาอย 1 h12 h21 a2h22 h11 h31 y1 81: y2 The ReLU activation function is defined as follows: h32 For an input x, ReLU(x) = max(0, x) The Softmax function is defined as follows: S Given the inputs, xi, i=1, ..., n; the outputs are as follows: exi = j=1 Calculate the net inputs a1 and a2. Calculate the ReLU outputs. Calculate y1 and y2. Softmax exj Suppose that the weights of the network are w11=-1.5; w12=-1.0, w21-1.2; w22=1.0; w31=-0.6; w32=1.0 h11=-0.5; h12=-1.0; h21=1.0, h22=1.5; h31= 0.4; h32=0.3 For an input of x1= 1.0 and x2=2.0 z1 z2

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Concept explainers

Use of XOR function

XOR function or exclusive OR function was introduced in Excel 2013. This function is available under excel logical functions. For the given statement, the XOR function will return true only when either of the statements is true. It returns false when both the statements are true or when both the statements are false.

Question

(c) A fully connected neural network is given below.
●
x1
x2
1
Note that:
i.
ii.
iii.
iv.
w11
w21
W31
w12
w22
w32
al
a2
1
ReLU
RELU
h12
h21
h11
h22
h31
h32
The ReLU activation function is defined as follows:
For an input x, ReLU(x) = max(0, x)
The Softmax function is defined as follows:
y1
s (x₂)
y2
=
Given the inputs, xi, i=1, ..., n; the outputs are as follows:
exi
Calculate the net inputs al and a2.
Calculate the ReLU outputs.
Calculate y1 and y2.
Calculate z1 and 22.
Softmax
exj
Suppose that the weights of the network are
w11=-1.5; w12=-1.0, w21-1.2; w22=1.0; w31=-0.6; w32=1.0
h11=-0.5; h12=-1.0; h21=1.0, h22=1.5; h31= 0.4; h32=0.3
For an input of x1= 1.0 and x2=2.0
z1
z2

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